mlsanityData to 7 Oct 2026

BatchNorm-based Weakly Supervised Video Anomaly Detection

arXiv 2311.15367 ↗First posted 26 Nov 2023cs.CVcited by 22

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Abstract (arXiv)

In weakly supervised video anomaly detection (WVAD), where only video-level labels indicating the presence or absence of abnormal events are available, the primary challenge arises from the inherent ambiguity in temporal annotations of abnormal occurrences. Inspired by the statistical insight that temporal features of abnormal events often exhibit outlier characteristics, we propose a novel method, BN-WVAD, which incorporates BatchNorm into WVAD. In the proposed BN-WVAD, we leverage the Divergence of Feature from Mean vector (DFM) of BatchNorm as a reliable abnormality criterion to discern potential abnormal snippets in abnormal videos. The proposed DFM criterion is also discriminative for anomaly recognition and more resilient to label noise, serving as the additional anomaly score to amend the prediction of the anomaly classifier that is susceptible to noisy labels. Moreover, a batch-level selection strategy is devised to filter more abnormal snippets in videos where more abnormal events occur. The proposed BN-WVAD model demonstrates state-of-the-art performance on UCF-Crime with an AUC of 87.24%, and XD-Violence, where AP reaches up to 84.93%. Our code implementation is accessible at https://github.com/cool-xuan/BN-WVAD.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). Builds on 31 of its 60 references that are in the map. First posted on arXiv 26 Nov 2023.

Builds on

31 of 60 references.

References · 31

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
  3. 2017Quo Vadis, Action Recognition? A New Model and the Kinetics Datasetcited by 3,259
  4. 2016Aggregated Residual Transformations for Deep Neural Networkscited by 2,540
  5. 2021Video Swin Transformercited by 1,090
  6. 2018Real-world Anomaly Detection in Surveillance Videoscited by 570
  7. 2021ReAct: Out-of-distribution Detection With Rectified Activationscited by 486
  8. 2017Temporal Segment Networks for Action Recognition in Videoscited by 279
  9. 2020Learning Memory-guided Normality for Anomaly Detectioncited by 278
  10. 2020Not only Look, but also Listen: Learning Multimodal Violence Detection under Weak Supervisioncited by 235
  11. 2021Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learningcited by 226
  12. 2018Adversarially Learned One-Class Classifier for Novelty Detectioncited by 202
19 more
  1. 2019Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detectioncited by 171
  2. 2018Latent Space Autoregression for Novelty Detectioncited by 149
  3. 2020Anomaly Detection in Video via Self-Supervised and Multi-Task Learningcited by 147
  4. 2021MIST: Multiple Instance Self-Training Framework for Video Anomaly Detectioncited by 137
  5. 2021A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Predictioncited by 129
  6. 2019Anomaly Detection in Video Sequence with Appearance-Motion Correspondencecited by 118
  7. 2021Pixel-wise Anomaly Detection in Complex Driving Scenescited by 109
  8. 2022Generative Cooperative Learning for Unsupervised Video Anomaly Detectioncited by 102
  9. 2019Graph Embedded Pose Clustering for Anomaly Detectioncited by 95
  10. 2023Dual Memory Units with Uncertainty Regulation for Weakly Supervised Video Anomaly Detectioncited by 88
  11. 2020Self-trained Deep Ordinal Regression for End-to-End Video Anomaly Detectioncited by 83
  12. 2020CLAWS: Clustering Assisted Weakly Supervised Learning with Normalcy Suppression for Anomalous Event Detectioncited by 79
  13. 2020A Topological Filter for Learning with Label Noisecited by 76
  14. 2022Normalizing Flows for Human Pose Anomaly Detectioncited by 59
  15. 2022Exploiting Completeness and Uncertainty of Pseudo Labels for Weakly Supervised Video Anomaly Detectioncited by 47
  16. 2022SAFE: Sensitivity-Aware Features for Out-of-Distribution Object Detectioncited by 33
  17. 2022Modality-Aware Contrastive Instance Learning with Self-Distillation for Weakly-Supervised Audio-Visual Violence Detectioncited by 26
  18. 2023Weakly-Supervised Video Anomaly Detection with Snippet Anomalous Attentioncited by 15
  19. 2023AnoOnly: Semi-Supervised Anomaly Detection with the Only Loss on Anomaliescited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 10; 2025: 5; 2026: 7.

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  1. 2024Deep Learning for Video Anomaly Detection: A Reviewcited by 27
  2. 2024MissionGNN: Hierarchical Multimodal GNN-based Weakly Supervised Video Anomaly Recognition with Mission-Specific Knowledge Graph Generationcited by 14
  3. 2024Privacy-Preserving Video Anomaly Detection: A Surveycited by 11
  4. 2024VQ-Flow: Taming Normalizing Flows for Multi-Class Anomaly Detection via Hierarchical Vector Quantizationcited by 5
  5. 2025STEAD: Spatio-Temporal Efficient Anomaly Detection for Time and Compute Sensitive Applicationscited by 4
  6. 2025The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLMcited by 4
  7. 2024Unveiling Context-Related Anomalies: Knowledge Graph Empowered Decoupling of Scene and Action for Human-Related Video Anomaly Detectioncited by 3
  8. 2024Multimodal Attention-Enhanced Feature Fusion-based Weekly Supervised Anomaly Violence Detectioncited by 2
  9. 2025Weakly Supervised Temporal Sentence Grounding via Positive Sample Miningcited by 2
  10. 2025VADER: Towards Causal Video Anomaly Understanding with Relation-Aware Large Language Modelscited by 2
  11. 2024Identifying Spatio-Temporal Drivers of Extreme Eventscited by 1
  12. 2024Continuous GNN-based Anomaly Detection on Edge using Efficient Adaptive Knowledge Graph Learningcited by 1
10 more
  1. 2024TeG: Temporal-Granularity Method for Anomaly Detection with Attention in Smart City Surveillancecited by 1
  2. 2026Auditing Frame-Level AUC in Weakly Supervised Video Anomaly Detection: Granularity, Resolution, and Scene Biascited by 1
  3. 2024MTFL: Multi-Timescale Feature Learning for Weakly-Supervised Anomaly Detection in Surveillance Videoscited by 0
  4. 2025Unmasking Performance Gaps: A Comparative Study of Human Anonymization and Its Effects on Video Anomaly Detectioncited by 0
  5. 2026Cross Pseudo Labeling For Weakly Supervised Video Anomaly Detectioncited by 0
  6. 2026SENSE-VAD: Sentient and Semantic Video Anomaly Detection for Autonomous Drivingcited by 0
  7. 2026Event Stream based Multi-Modal Video Anomaly Detection: A Benchmark Dataset and Algorithmscited by 0
  8. 2026Context-structured Video Anomaly Detection with Large Vision-Language Modelscited by 0
  9. 2026Look Inside Each Video: Rethinking How Video Anomaly Detection Is Evaluatedcited by 0
  10. 2026Cog-VADU: A Training-Free Cognitive Reasoning Framework for Video Anomaly Detection and Understandingcited by 0